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from transformers import PreTrainedModel, VisionEncoderDecoderModel, ViTMAEModel, ConditionalDetrModel |
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from transformers.models.conditional_detr.modeling_conditional_detr import ( |
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ConditionalDetrMLPPredictionHead, |
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ConditionalDetrModelOutput, |
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ConditionalDetrHungarianMatcher, |
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inverse_sigmoid, |
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) |
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from .configuration_magi import MagiConfig |
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from .processing_magi import MagiProcessor |
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from torch import nn |
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from typing import Optional, List |
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import torch |
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from einops import rearrange, repeat, einsum |
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from .utils import move_to_device, visualise_single_image_prediction, sort_panels, sort_text_boxes_in_reading_order |
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class MagiModel(PreTrainedModel): |
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config_class = MagiConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.config = config |
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self.processor = MagiProcessor(config) |
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if not config.disable_ocr: |
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self.ocr_model = VisionEncoderDecoderModel(config.ocr_model_config) |
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if not config.disable_crop_embeddings: |
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self.crop_embedding_model = ViTMAEModel(config.crop_embedding_model_config) |
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if not config.disable_detections: |
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self.num_non_obj_tokens = 5 |
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self.detection_transformer = ConditionalDetrModel(config.detection_model_config) |
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self.bbox_predictor = ConditionalDetrMLPPredictionHead( |
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input_dim=config.detection_model_config.d_model, |
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hidden_dim=config.detection_model_config.d_model, |
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output_dim=4, num_layers=3 |
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) |
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self.is_this_text_a_dialogue = ConditionalDetrMLPPredictionHead( |
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input_dim=config.detection_model_config.d_model, |
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hidden_dim=config.detection_model_config.d_model, |
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output_dim=1, |
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num_layers=3 |
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) |
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self.character_character_matching_head = ConditionalDetrMLPPredictionHead( |
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input_dim = 3 * config.detection_model_config.d_model + (2 * config.crop_embedding_model_config.hidden_size if not config.disable_crop_embeddings else 0), |
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hidden_dim=config.detection_model_config.d_model, |
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output_dim=1, num_layers=3 |
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) |
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self.text_character_matching_head = ConditionalDetrMLPPredictionHead( |
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input_dim = 3 * config.detection_model_config.d_model, |
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hidden_dim=config.detection_model_config.d_model, |
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output_dim=1, num_layers=3 |
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) |
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self.class_labels_classifier = nn.Linear( |
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config.detection_model_config.d_model, config.detection_model_config.num_labels |
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) |
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self.matcher = ConditionalDetrHungarianMatcher( |
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class_cost=config.detection_model_config.class_cost, |
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bbox_cost=config.detection_model_config.bbox_cost, |
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giou_cost=config.detection_model_config.giou_cost |
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) |
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def move_to_device(self, input): |
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return move_to_device(input, self.device) |
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|
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def predict_detections_and_associations( |
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self, |
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images, |
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move_to_device_fn=None, |
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character_detection_threshold=0.3, |
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panel_detection_threshold=0.2, |
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text_detection_threshold=0.25, |
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character_character_matching_threshold=0.65, |
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text_character_matching_threshold=0.4, |
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): |
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assert not self.config.disable_detections |
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move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn |
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inputs_to_detection_transformer = self.processor.preprocess_inputs_for_detection(images) |
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inputs_to_detection_transformer = move_to_device_fn(inputs_to_detection_transformer) |
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detection_transformer_output = self._get_detection_transformer_output(**inputs_to_detection_transformer) |
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predicted_class_scores, predicted_bboxes = self._get_predicted_bboxes_and_classes(detection_transformer_output) |
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def get_character_character_matching_scores(batch_character_indices, batch_bboxes): |
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predicted_obj_tokens_for_batch = self._get_predicted_obj_tokens(detection_transformer_output) |
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predicted_c2c_tokens_for_batch = self._get_predicted_c2c_tokens(detection_transformer_output) |
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crop_bboxes = [batch_bboxes[i][batch_character_indices[i]] for i in range(len(batch_character_indices))] |
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crop_embeddings_for_batch = self.predict_crop_embeddings(images, crop_bboxes, move_to_device_fn) |
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character_obj_tokens_for_batch = [] |
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c2c_tokens_for_batch = [] |
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for predicted_obj_tokens, predicted_c2c_tokens, character_indices in zip(predicted_obj_tokens_for_batch, predicted_c2c_tokens_for_batch, batch_character_indices): |
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character_obj_tokens_for_batch.append(predicted_obj_tokens[character_indices]) |
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c2c_tokens_for_batch.append(predicted_c2c_tokens) |
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return self._get_character_character_affinity_matrices( |
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character_obj_tokens_for_batch=character_obj_tokens_for_batch, |
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crop_embeddings_for_batch=crop_embeddings_for_batch, |
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c2c_tokens_for_batch=c2c_tokens_for_batch, |
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apply_sigmoid=True, |
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) |
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def get_text_character_matching_scores(batch_text_indices, batch_character_indices): |
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predicted_obj_tokens_for_batch = self._get_predicted_obj_tokens(detection_transformer_output) |
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predicted_t2c_tokens_for_batch = self._get_predicted_t2c_tokens(detection_transformer_output) |
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text_obj_tokens_for_batch = [] |
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character_obj_tokens_for_batch = [] |
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t2c_tokens_for_batch = [] |
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for predicted_obj_tokens, predicted_t2c_tokens, text_indices, character_indices in zip(predicted_obj_tokens_for_batch, predicted_t2c_tokens_for_batch, batch_text_indices, batch_character_indices): |
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text_obj_tokens_for_batch.append(predicted_obj_tokens[text_indices]) |
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character_obj_tokens_for_batch.append(predicted_obj_tokens[character_indices]) |
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t2c_tokens_for_batch.append(predicted_t2c_tokens) |
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return self._get_text_character_affinity_matrices( |
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character_obj_tokens_for_batch=character_obj_tokens_for_batch, |
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text_obj_tokens_for_this_batch=text_obj_tokens_for_batch, |
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t2c_tokens_for_batch=t2c_tokens_for_batch, |
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apply_sigmoid=True, |
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) |
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def get_dialog_confidence_scores(batch_text_indices): |
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predicted_obj_tokens_for_batch = self._get_predicted_obj_tokens(detection_transformer_output) |
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dialog_confidence = [] |
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for predicted_obj_tokens, text_indices in zip(predicted_obj_tokens_for_batch, batch_text_indices): |
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confidence = self.is_this_text_a_dialogue(predicted_obj_tokens[text_indices]).sigmoid() |
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dialog_confidence.append(rearrange(confidence, "i 1 -> i")) |
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return dialog_confidence |
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return self.processor.postprocess_detections_and_associations( |
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predicted_bboxes=predicted_bboxes, |
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predicted_class_scores=predicted_class_scores, |
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original_image_sizes=torch.stack([torch.tensor(img.shape[:2]) for img in images], dim=0).to(predicted_bboxes.device), |
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get_character_character_matching_scores=get_character_character_matching_scores, |
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get_text_character_matching_scores=get_text_character_matching_scores, |
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get_dialog_confidence_scores=get_dialog_confidence_scores, |
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character_detection_threshold=character_detection_threshold, |
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panel_detection_threshold=panel_detection_threshold, |
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text_detection_threshold=text_detection_threshold, |
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character_character_matching_threshold=character_character_matching_threshold, |
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text_character_matching_threshold=text_character_matching_threshold, |
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) |
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def predict_crop_embeddings(self, images, crop_bboxes, move_to_device_fn=None, mask_ratio=0.0, batch_size=256): |
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if self.config.disable_crop_embeddings: |
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return None |
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assert isinstance(crop_bboxes, List), "please provide a list of bboxes for each image to get embeddings for" |
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move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn |
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old_mask_ratio = self.crop_embedding_model.embeddings.config.mask_ratio |
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self.crop_embedding_model.embeddings.config.mask_ratio = mask_ratio |
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crops_per_image = [] |
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num_crops_per_batch = [len(bboxes) for bboxes in crop_bboxes] |
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for image, bboxes, num_crops in zip(images, crop_bboxes, num_crops_per_batch): |
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crops = self.processor.crop_image(image, bboxes) |
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assert len(crops) == num_crops |
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crops_per_image.extend(crops) |
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if len(crops_per_image) == 0: |
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return [[] for _ in crop_bboxes] |
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crops_per_image = self.processor.preprocess_inputs_for_crop_embeddings(crops_per_image) |
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crops_per_image = move_to_device_fn(crops_per_image) |
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embeddings = [] |
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for i in range(0, len(crops_per_image), batch_size): |
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crops = crops_per_image[i:i+batch_size] |
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embeddings_per_batch = self.crop_embedding_model(crops).last_hidden_state[:, 0] |
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embeddings.append(embeddings_per_batch) |
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embeddings = torch.cat(embeddings, dim=0) |
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crop_embeddings_for_batch = [] |
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for num_crops in num_crops_per_batch: |
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crop_embeddings_for_batch.append(embeddings[:num_crops]) |
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embeddings = embeddings[num_crops:] |
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self.crop_embedding_model.embeddings.config.mask_ratio = old_mask_ratio |
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return crop_embeddings_for_batch |
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def predict_ocr(self, images, crop_bboxes, move_to_device_fn=None, use_tqdm=False, batch_size=32): |
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assert not self.config.disable_ocr |
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move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn |
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crops_per_image = [] |
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num_crops_per_batch = [len(bboxes) for bboxes in crop_bboxes] |
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for image, bboxes, num_crops in zip(images, crop_bboxes, num_crops_per_batch): |
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crops = self.processor.crop_image(image, bboxes) |
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assert len(crops) == num_crops |
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crops_per_image.extend(crops) |
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if len(crops_per_image) == 0: |
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return [[] for _ in crop_bboxes] |
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crops_per_image = self.processor.preprocess_inputs_for_ocr(crops_per_image) |
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crops_per_image = move_to_device_fn(crops_per_image) |
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all_generated_texts = [] |
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if use_tqdm: |
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from tqdm import tqdm |
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pbar = tqdm(range(0, len(crops_per_image), batch_size)) |
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else: |
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pbar = range(0, len(crops_per_image), batch_size) |
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for i in pbar: |
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crops = crops_per_image[i:i+batch_size] |
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generated_ids = self.ocr_model.generate(crops) |
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generated_texts = self.processor.postprocess_ocr_tokens(generated_ids) |
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all_generated_texts.extend(generated_texts) |
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texts_for_images = [] |
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for num_crops in num_crops_per_batch: |
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texts_for_images.append([x.replace("\n", "") for x in all_generated_texts[:num_crops]]) |
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all_generated_texts = all_generated_texts[num_crops:] |
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return texts_for_images |
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def visualise_single_image_prediction( |
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self, image_as_np_array, predictions, filename=None |
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): |
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return visualise_single_image_prediction(image_as_np_array, predictions, filename) |
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def generate_transcript_for_single_image( |
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self, predictions, ocr_results, filename=None |
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): |
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character_clusters = predictions["character_cluster_labels"] |
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text_to_character = predictions["text_character_associations"] |
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text_to_character = {k: v for k, v in text_to_character} |
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transript = " ### Transcript ###\n" |
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for index, text in enumerate(ocr_results): |
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if index in text_to_character: |
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speaker = character_clusters[text_to_character[index]] |
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speaker = f"<{speaker}>" |
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else: |
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speaker = "<?>" |
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transript += f"{speaker}: {text}\n" |
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if filename is not None: |
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with open(filename, "w") as file: |
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file.write(transript) |
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return transript |
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def get_affinity_matrices_given_annotations( |
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self, images, annotations, move_to_device_fn=None, apply_sigmoid=True |
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): |
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assert not self.config.disable_detections |
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move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn |
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character_bboxes_in_batch = [[bbox for bbox, label in zip(a["bboxes_as_x1y1x2y2"], a["labels"]) if label == 0] for a in annotations] |
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crop_embeddings_for_batch = self.predict_crop_embeddings(images, character_bboxes_in_batch, move_to_device_fn) |
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inputs_to_detection_transformer = self.processor.preprocess_inputs_for_detection(images, annotations) |
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inputs_to_detection_transformer = move_to_device_fn(inputs_to_detection_transformer) |
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processed_targets = inputs_to_detection_transformer.pop("labels") |
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detection_transformer_output = self._get_detection_transformer_output(**inputs_to_detection_transformer) |
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predicted_obj_tokens_for_batch = self._get_predicted_obj_tokens(detection_transformer_output) |
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predicted_t2c_tokens_for_batch = self._get_predicted_t2c_tokens(detection_transformer_output) |
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predicted_c2c_tokens_for_batch = self._get_predicted_c2c_tokens(detection_transformer_output) |
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predicted_class_scores, predicted_bboxes = self._get_predicted_bboxes_and_classes(detection_transformer_output) |
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matching_dict = { |
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"logits": predicted_class_scores, |
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"pred_boxes": predicted_bboxes, |
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} |
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indices = self.matcher(matching_dict, processed_targets) |
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matched_char_obj_tokens_for_batch = [] |
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matched_text_obj_tokens_for_batch = [] |
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t2c_tokens_for_batch = [] |
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c2c_tokens_for_batch = [] |
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text_bboxes_for_batch = [] |
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character_bboxes_for_batch = [] |
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for j, (pred_idx, tgt_idx) in enumerate(indices): |
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target_idx_to_pred_idx = {tgt.item(): pred.item() for pred, tgt in zip(pred_idx, tgt_idx)} |
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targets_for_this_image = processed_targets[j] |
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indices_of_text_boxes_in_annotation = [i for i, label in enumerate(targets_for_this_image["class_labels"]) if label == 1] |
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indices_of_char_boxes_in_annotation = [i for i, label in enumerate(targets_for_this_image["class_labels"]) if label == 0] |
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predicted_text_indices = [target_idx_to_pred_idx[i] for i in indices_of_text_boxes_in_annotation] |
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predicted_char_indices = [target_idx_to_pred_idx[i] for i in indices_of_char_boxes_in_annotation] |
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text_bboxes_for_batch.append( |
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[annotations[j]["bboxes_as_x1y1x2y2"][k] for k in indices_of_text_boxes_in_annotation] |
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) |
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character_bboxes_for_batch.append( |
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[annotations[j]["bboxes_as_x1y1x2y2"][k] for k in indices_of_char_boxes_in_annotation] |
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) |
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matched_char_obj_tokens_for_batch.append(predicted_obj_tokens_for_batch[j][predicted_char_indices]) |
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matched_text_obj_tokens_for_batch.append(predicted_obj_tokens_for_batch[j][predicted_text_indices]) |
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t2c_tokens_for_batch.append(predicted_t2c_tokens_for_batch[j]) |
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c2c_tokens_for_batch.append(predicted_c2c_tokens_for_batch[j]) |
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text_character_affinity_matrices = self._get_text_character_affinity_matrices( |
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character_obj_tokens_for_batch=matched_char_obj_tokens_for_batch, |
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text_obj_tokens_for_this_batch=matched_text_obj_tokens_for_batch, |
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t2c_tokens_for_batch=t2c_tokens_for_batch, |
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apply_sigmoid=apply_sigmoid, |
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) |
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character_character_affinity_matrices = self._get_character_character_affinity_matrices( |
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character_obj_tokens_for_batch=matched_char_obj_tokens_for_batch, |
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crop_embeddings_for_batch=crop_embeddings_for_batch, |
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c2c_tokens_for_batch=c2c_tokens_for_batch, |
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apply_sigmoid=apply_sigmoid, |
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) |
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return { |
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"text_character_affinity_matrices": text_character_affinity_matrices, |
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"character_character_affinity_matrices": character_character_affinity_matrices, |
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"text_bboxes_for_batch": text_bboxes_for_batch, |
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"character_bboxes_for_batch": character_bboxes_for_batch, |
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} |
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def get_obj_embeddings_corresponding_to_given_annotations( |
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self, images, annotations, move_to_device_fn=None |
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): |
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assert not self.config.disable_detections |
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move_to_device_fn = self.move_to_device if move_to_device_fn is None else move_to_device_fn |
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inputs_to_detection_transformer = self.processor.preprocess_inputs_for_detection(images, annotations) |
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inputs_to_detection_transformer = move_to_device_fn(inputs_to_detection_transformer) |
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processed_targets = inputs_to_detection_transformer.pop("labels") |
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detection_transformer_output = self._get_detection_transformer_output(**inputs_to_detection_transformer) |
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predicted_obj_tokens_for_batch = self._get_predicted_obj_tokens(detection_transformer_output) |
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predicted_t2c_tokens_for_batch = self._get_predicted_t2c_tokens(detection_transformer_output) |
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predicted_c2c_tokens_for_batch = self._get_predicted_c2c_tokens(detection_transformer_output) |
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predicted_class_scores, predicted_bboxes = self._get_predicted_bboxes_and_classes(detection_transformer_output) |
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matching_dict = { |
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"logits": predicted_class_scores, |
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"pred_boxes": predicted_bboxes, |
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} |
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indices = self.matcher(matching_dict, processed_targets) |
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matched_char_obj_tokens_for_batch = [] |
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matched_text_obj_tokens_for_batch = [] |
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matched_panel_obj_tokens_for_batch = [] |
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t2c_tokens_for_batch = [] |
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c2c_tokens_for_batch = [] |
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for j, (pred_idx, tgt_idx) in enumerate(indices): |
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target_idx_to_pred_idx = {tgt.item(): pred.item() for pred, tgt in zip(pred_idx, tgt_idx)} |
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targets_for_this_image = processed_targets[j] |
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indices_of_char_boxes_in_annotation = [i for i, label in enumerate(targets_for_this_image["class_labels"]) if label == 0] |
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indices_of_text_boxes_in_annotation = [i for i, label in enumerate(targets_for_this_image["class_labels"]) if label == 1] |
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indices_of_panel_boxes_in_annotation = [i for i, label in enumerate(targets_for_this_image["class_labels"]) if label == 2] |
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predicted_text_indices = [target_idx_to_pred_idx[i] for i in indices_of_text_boxes_in_annotation] |
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predicted_char_indices = [target_idx_to_pred_idx[i] for i in indices_of_char_boxes_in_annotation] |
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predicted_panel_indices = [target_idx_to_pred_idx[i] for i in indices_of_panel_boxes_in_annotation] |
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|
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matched_char_obj_tokens_for_batch.append(predicted_obj_tokens_for_batch[j][predicted_char_indices]) |
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matched_text_obj_tokens_for_batch.append(predicted_obj_tokens_for_batch[j][predicted_text_indices]) |
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matched_panel_obj_tokens_for_batch.append(predicted_obj_tokens_for_batch[j][predicted_panel_indices]) |
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t2c_tokens_for_batch.append(predicted_t2c_tokens_for_batch[j]) |
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c2c_tokens_for_batch.append(predicted_c2c_tokens_for_batch[j]) |
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|
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return { |
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"character": matched_char_obj_tokens_for_batch, |
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"text": matched_text_obj_tokens_for_batch, |
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"panel": matched_panel_obj_tokens_for_batch, |
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"t2c": t2c_tokens_for_batch, |
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"c2c": c2c_tokens_for_batch, |
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} |
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|
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def sort_panels_and_text_bboxes_in_reading_order( |
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self, |
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batch_panel_bboxes, |
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batch_text_bboxes, |
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): |
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batch_sorted_panel_indices = [] |
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batch_sorted_text_indices = [] |
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for batch_index in range(len(batch_text_bboxes)): |
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panel_bboxes = batch_panel_bboxes[batch_index] |
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text_bboxes = batch_text_bboxes[batch_index] |
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sorted_panel_indices = sort_panels(panel_bboxes) |
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sorted_panels = [panel_bboxes[i] for i in sorted_panel_indices] |
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sorted_text_indices = sort_text_boxes_in_reading_order(text_bboxes, sorted_panels) |
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batch_sorted_panel_indices.append(sorted_panel_indices) |
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batch_sorted_text_indices.append(sorted_text_indices) |
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return batch_sorted_panel_indices, batch_sorted_text_indices |
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|
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def _get_detection_transformer_output( |
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self, |
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pixel_values: torch.FloatTensor, |
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pixel_mask: Optional[torch.LongTensor] = None |
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): |
|
if self.config.disable_detections: |
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raise ValueError("Detection model is disabled. Set disable_detections=False in the config.") |
|
return self.detection_transformer( |
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pixel_values=pixel_values, |
|
pixel_mask=pixel_mask, |
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return_dict=True |
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) |
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|
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def _get_predicted_obj_tokens( |
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self, |
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detection_transformer_output: ConditionalDetrModelOutput |
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): |
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return detection_transformer_output.last_hidden_state[:, :-self.num_non_obj_tokens] |
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|
|
def _get_predicted_c2c_tokens( |
|
self, |
|
detection_transformer_output: ConditionalDetrModelOutput |
|
): |
|
return detection_transformer_output.last_hidden_state[:, -self.num_non_obj_tokens] |
|
|
|
def _get_predicted_t2c_tokens( |
|
self, |
|
detection_transformer_output: ConditionalDetrModelOutput |
|
): |
|
return detection_transformer_output.last_hidden_state[:, -self.num_non_obj_tokens+1] |
|
|
|
def _get_predicted_bboxes_and_classes( |
|
self, |
|
detection_transformer_output: ConditionalDetrModelOutput, |
|
): |
|
if self.config.disable_detections: |
|
raise ValueError("Detection model is disabled. Set disable_detections=False in the config.") |
|
|
|
obj = self._get_predicted_obj_tokens(detection_transformer_output) |
|
|
|
predicted_class_scores = self.class_labels_classifier(obj) |
|
reference = detection_transformer_output.reference_points[:-self.num_non_obj_tokens] |
|
reference_before_sigmoid = inverse_sigmoid(reference).transpose(0, 1) |
|
predicted_boxes = self.bbox_predictor(obj) |
|
predicted_boxes[..., :2] += reference_before_sigmoid |
|
predicted_boxes = predicted_boxes.sigmoid() |
|
|
|
return predicted_class_scores, predicted_boxes |
|
|
|
def _get_character_character_affinity_matrices( |
|
self, |
|
character_obj_tokens_for_batch: List[torch.FloatTensor] = None, |
|
crop_embeddings_for_batch: List[torch.FloatTensor] = None, |
|
c2c_tokens_for_batch: List[torch.FloatTensor] = None, |
|
apply_sigmoid=True, |
|
): |
|
assert self.config.disable_detections or (character_obj_tokens_for_batch is not None and c2c_tokens_for_batch is not None) |
|
assert self.config.disable_crop_embeddings or crop_embeddings_for_batch is not None |
|
assert not self.config.disable_detections or not self.config.disable_crop_embeddings |
|
|
|
if self.config.disable_detections: |
|
affinity_matrices = [] |
|
for crop_embeddings in crop_embeddings_for_batch: |
|
crop_embeddings = crop_embeddings / crop_embeddings.norm(dim=-1, keepdim=True) |
|
affinity_matrix = einsum("i d, j d -> i j", affinity_matrix) |
|
affinity_matrices.append(affinity_matrix) |
|
return affinity_matrices |
|
affinity_matrices = [] |
|
for batch_index, (character_obj_tokens, c2c) in enumerate(zip(character_obj_tokens_for_batch, c2c_tokens_for_batch)): |
|
if character_obj_tokens.shape[0] == 0: |
|
affinity_matrices.append(torch.zeros(0, 0).type_as(character_obj_tokens)) |
|
continue |
|
if not self.config.disable_crop_embeddings: |
|
crop_embeddings = crop_embeddings_for_batch[batch_index] |
|
assert character_obj_tokens.shape[0] == crop_embeddings.shape[0] |
|
character_obj_tokens = torch.cat([character_obj_tokens, crop_embeddings], dim=-1) |
|
char_i = repeat(character_obj_tokens, "i d -> i repeat d", repeat=character_obj_tokens.shape[0]) |
|
char_j = repeat(character_obj_tokens, "j d -> repeat j d", repeat=character_obj_tokens.shape[0]) |
|
char_ij = rearrange([char_i, char_j], "two i j d -> (i j) (two d)") |
|
c2c = repeat(c2c, "d -> repeat d", repeat = char_ij.shape[0]) |
|
char_ij_c2c = torch.cat([char_ij, c2c], dim=-1) |
|
character_character_affinities = self.character_character_matching_head(char_ij_c2c) |
|
character_character_affinities = rearrange(character_character_affinities, "(i j) 1 -> i j", i=char_i.shape[0]) |
|
character_character_affinities = (character_character_affinities + character_character_affinities.T) / 2 |
|
if apply_sigmoid: |
|
character_character_affinities = character_character_affinities.sigmoid() |
|
affinity_matrices.append(character_character_affinities) |
|
return affinity_matrices |
|
|
|
def _get_text_character_affinity_matrices( |
|
self, |
|
character_obj_tokens_for_batch: List[torch.FloatTensor] = None, |
|
text_obj_tokens_for_this_batch: List[torch.FloatTensor] = None, |
|
t2c_tokens_for_batch: List[torch.FloatTensor] = None, |
|
apply_sigmoid=True, |
|
): |
|
assert not self.config.disable_detections |
|
assert character_obj_tokens_for_batch is not None and text_obj_tokens_for_this_batch is not None and t2c_tokens_for_batch is not None |
|
affinity_matrices = [] |
|
for character_obj_tokens, text_obj_tokens, t2c in zip(character_obj_tokens_for_batch, text_obj_tokens_for_this_batch, t2c_tokens_for_batch): |
|
if character_obj_tokens.shape[0] == 0 or text_obj_tokens.shape[0] == 0: |
|
affinity_matrices.append(torch.zeros(text_obj_tokens.shape[0], character_obj_tokens.shape[0]).type_as(character_obj_tokens)) |
|
continue |
|
text_i = repeat(text_obj_tokens, "i d -> i repeat d", repeat=character_obj_tokens.shape[0]) |
|
char_j = repeat(character_obj_tokens, "j d -> repeat j d", repeat=text_obj_tokens.shape[0]) |
|
text_char = rearrange([text_i, char_j], "two i j d -> (i j) (two d)") |
|
t2c = repeat(t2c, "d -> repeat d", repeat = text_char.shape[0]) |
|
text_char_t2c = torch.cat([text_char, t2c], dim=-1) |
|
text_character_affinities = self.text_character_matching_head(text_char_t2c) |
|
text_character_affinities = rearrange(text_character_affinities, "(i j) 1 -> i j", i=text_i.shape[0]) |
|
if apply_sigmoid: |
|
text_character_affinities = text_character_affinities.sigmoid() |
|
affinity_matrices.append(text_character_affinities) |
|
return affinity_matrices |
